Rachel Moore, Alessandro Bonetti, Rhona J Cox, Stefan Geschwindner, Mariacarmela Giurrandino, Sergio Martinez Cuesta, Stefan Schiesser
Small-molecule RNA modulators offer access to novel target space and new mechanisms to modulate protein targets that are difficult to drug directly, but clinical translation remains limited. This review examines the principal challenges and opportunities in small-molecule RNA-targeted hit discovery, including target prioritization, mechanisms of modulation, the gap between RNA structural engagement and functional outcome, hit-finding strategies, and hit-to-lead optimization. Progress requires closing the structure-function-druggability loop through in-cell validation and RNA-specific druggability models, deploying targeted RNA degradation, establishing standardized validation cascades, building RNA-native chemical infrastructure, and training AI on RNA-specific datasets.
INTRODUCTION: RNA is now recognized as an active regulatory target rather than a passive intermediary, expanding therapeutic opportunities beyond protein-centric drug discovery. Small-molecule RNA modulators offer access to novel target space and new mechanisms to modulate protein targets that are difficult to drug directly, but clinical translation remains limited.
AREAS COVERED: This review examines the principal challenges and opportunities in small-molecule RNA-targeted hit discovery: target prioritization based on genetics, structure, and function; mechanisms of modulation beyond splicing, including translation interference and targeted RNA degradation; the persistent gap between RNA structural engagement and functional outcome; hit-finding strategies; and hit-to-lead optimization. Here, the authors discuss how limitations do not lie solely in identifying RNA binders, but in predicting prospectively whether binding to a defined RNA structure will produce a functional consequence.
EXPERT OPINION: Progress requires closing the structure-function-druggability loop through in-cell validation and RNA-specific druggability models, deploying targeted RNA degradation to convert silent binders into functional molecules, establishing standardized validation cascades and translational PK/PD frameworks, building RNA-native chemical infrastructure, and training AI on RNA-specific datasets. Pre-competitive infrastructure to generate shared structural and functional data will accelerate the field.